TidalWavelet

程序功能为PCA(主元分析)中SPE故障控制限的确定，解决了过程监测中一些定量问题(The progrm is used to difinit the SPE-limit of PCA)

SPWD - smoothed Pseudo Wigner-Ville Distribuition

Hook-Jeeves法解无约束最优化问题(Hook-Jeeves Solving unconstrained optimization problem)

这个程序包里面包含读取遥感影像的IDL代码，可以读取多个波段，并且显示图像！(This package which contains the IDL remote sensing image to read the code, which can be read by a number of bands, and display images!)

fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!. Given the samples of a normal distribution, the PDF parameter is found fits data to the probability of the form: p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2)) with parameters: u,sig^2 format: result = fit_ML_normal( x,hAx ) input: x - vector, samples with normal distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields sig^2,u - fitted parameters CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

包含所有约束优化问题中涉及的matlab源程序。包含Rosen梯度法求解约束多维函数的极值，外点罚函数法解线性等式约束，外点罚函数法解一般等式约束，内点罚函数法，混合罚函数法，混合罚函数加速法，乘子法，坐标轮换法及复合形法(Contains all the constrained optimization problem involves matlab source. Contains Rosen gradient method for solving constrained multi-dimensional function extremum, external point penalty function method for solving linear equality constraints, external point penalty function method for solving general equality constraints, interior point penalty function method, mixed penalty function method, mixed penalty function acceleration method , multiplier method, the coordinate rotation method and complex method)

说明：  水下机器人-机械手系统仿真（包含动力学，运动学，控制器）(Underwater Robotic Manipulators)

wlan信道仿真模型，考虑了各种不同场景下的信道变化(wlan channel simulation model, taking into account various channel change under different scenarios)

harris角点检测，通过harris角点检测方法来实现对于图像的角点检测，通过matlab实现(harris corner detection, by harris corner detection methods to achieve for the corner of the image is detected by matlab implementation)

Nous avons effectué les tests en tenant compte des paramètres utilisés afin de mettre en évidence leur influence. Sur les deux images testées, nous avons constaté que l’espace colorimétrique utilisé pour calculer la différence colorimétrique entre deux régions (RVB ou L * a * b * ) ainsi que le mode de segmentation (progressive ou par priorité) influent sur les résultats de segmentation. Nous avons mis aussi en évidence l’importance du seuil, qui est décisif sur les résultats que l’en veut atteindre. Nous pouvons également con